def get_algorithm(algorithm, graph):
if algorithm == 'generic':
return Goldberg(graph)
elif algorithm == 'height':
return GoldbergHeight(graph)
elif algorithm == 'wave':
return GoldbergWave(graph)
def test_max_flow_scale_free_directed(n):
seed_number = randint(1, 1000)
generator = ScaleFree(n, directed=True, seed_number=seed_number)
graph, source, target = generator.generate()
solver = Goldberg(graph)
max_flow = solver.get_max_flow(source, target)
generator = ScaleFree(n, directed=True, seed_number=seed_number)
graph, source, target = generator.generate()
res = gt.push_relabel_max_flow(graph, source, target, graph.ep.cap)
res.a = graph.ep.cap.a - res.a # the actual flow
gt_max_flow = sum(res[e] for e in target.in_edges())
assert max_flow == gt_max_flow
def test_max_flow_triangulation_delaunay_directed(n):
seed_number = randint(1, 1000)
generator = Triangulation(n,
type="delaunay",
directed=True,
seed_number=seed_number)
graph, source, target = generator.generate()
solver = Goldberg(graph)
max_flow = solver.get_max_flow(source, target)
generator = Triangulation(n,
type="delaunay",
directed=True,
seed_number=seed_number)
graph, source, target = generator.generate()
res = gt.push_relabel_max_flow(graph, source, target, graph.ep.cap)
res.a = graph.ep.cap.a - res.a # the actual flow
gt_max_flow = sum(res[e] for e in target.in_edges())
assert max_flow == gt_max_flow
def test_max_flow_scale_random_undirected(size):
seed_number = randint(1, 1000)
generator = Random(size[0],
size[1],
directed=False,
seed_number=seed_number)
graph, source, target = generator.generate()
solver = Goldberg(graph)
max_flow = solver.get_max_flow(source, target)
generator = Random(size[0],
size[1],
directed=False,
seed_number=seed_number)
graph, source, target = generator.generate()
res = gt.push_relabel_max_flow(graph, source, target, graph.ep.cap)
res.a = graph.ep.cap.a - res.a # the actual flow
gt_max_flow = sum(res[e] for e in target.in_edges())
assert max_flow == gt_max_flow
file = open(
"temporal_complexity_data_goldberg_4 edges for each vertex_10-90_nodes",
"w")
for nodes in [10, 20, 30, 40, 50, 60, 70, 80, 90]:
for i in range(0, 35):
#Goldberg version - using as graph generator Random
seed_number = randint(1, 1000)
generator = Random(nodes,
nodes * 4,
directed=True,
seed_number=seed_number)
g, source, target = generator.generate()
title = '- Parte grafo versione Goldberg con ' + str(
nodes) + ' nodi e ' + str(len(g.get_edges())) + ' archi - Random.'
print(title)
file.write(title)
solver = Goldberg(graph=g)
pr = cProfile.Profile()
pr.enable()
solution = solver.get_max_flow(source, target)
pr.disable()
s = StringIO()
sortby = 'cumulative'
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
file.write(s.getvalue())
file.close()
g, source, target = generator.generate()
print("Source: " + str(source) + " target " + str(target))
gt.graph_draw(g, edge_pen_width=gt.prop_to_size(g.ep.cap, mi=1, ma=5, power=1),
output="cf/cf_capacities.pdf", vertex_text=g.vertex_index, edge_text=g.ep.cap)'''
#seed_number = randint(1, 1000)
seed_number = 14
generator = Triangulation(50, type="delaunay", directed=True, seed_number=seed_number)
g, source, target = generator.generate()
gt.graph_draw(g, edge_pen_width=gt.prop_to_size(g.ep.cap, mi=1, ma=5, power=1),
output="cf/cf_capacities.pdf", vertex_text=g.vertex_index, edge_text=g.ep.cap)
# source = g.vertex(0)
# target = g.vertex(3)
solver = Goldberg(g)
solution = solver.get_max_flow(source, target)
print("Il max flow trovato da Golberg è:", solution)
labels = g.new_vertex_property("string")
for vertex in g.vertices():
labels[vertex] = str(g.vertex_index[vertex]) + "(" + str(g.vp.height[vertex]) + "," + str(
g.vp.excess[vertex]) + ")"
gt.graph_draw(g, edge_pen_width=gt.prop_to_size(g.ep.residual, mi=1, ma=5, power=1),
output="cf/max-flow-our-solution.pdf", vertex_text=labels, edge_text=g.ep.residual)
generator = Triangulation(50, type="delaunay", directed=True, seed_number=seed_number)
g, source, target = generator.generate()
cap = g.ep.cap
res = gt.push_relabel_max_flow(g, source, target, cap)